Improved beamforming by accounting for multiple domain shifts and sources of degradation

Abstract

A number of mechanisms are known to degrade ultrasound images. To address these issues, many beamforming strategies have been proposed including a slew of recent approaches centered around deep learning techniques. Most deep learning methods target a single type of image degradation. Here, we will simultaneously address multiple sources of image degradation within the same beamformer. In particular, we will address phase aberration, reverberation and off-axis scatting within a single network. Within this context of training networks to address multiple sources of image degradation, we will highlight the problem of domain shift in ultrasound beamforming deep networks. Domain shift is the mismatch in the statistics of the training data and the data encountered in the intended use case that reduces the performance of the trained deep network. We will show that ultrasound beamformers struggle with domain shift on both the input side and the output side of the networks, and we will present strategies for addressing both domain shifts including the shift on the output side where there is no ground truth data to learn from.